Exploiting semantics from ontologies to enhance accuracy of similarity measures

Abstract

Precisely determining semantic similarity between entities becomes a building block for data mining tasks, and existing approaches tackle this problem by mainly considering ontology-based annotations to decide relatedness. Nevertheless, because semantic similarity measures usually rely on the ontology class hierarchy and blindly treat ontology facts, they may erroneously assign high values of similarity to dissimilar entities. We propose ColorSim, a similarity measure that considers semantics of OWL2 annotations, e.g., relationship types, and implicit facts and their inferring processes, to accurately compute the relatedness of two ontology annotated entities. We compare ColorSim with state-of the- art approaches and report on preliminary experimental results that suggest the benefits of exploiting knowledge encoded in the ontologies to measure similarity.